Online social networks are becoming increasingly popular as a way for users to consume media over networks such as the internet. For example, many users are using social network communities as a portal to discover and listen to music, or find films to watch or news stories to read. As these online social networks become more centered around media content and proliferate, there is an increased interest in analyzing interactions among users and characterizing their behavior in terms of the individuals' and community preference for specific types of media content.
Many of these social networks include features that encourage social interactions by providing personalized recommendations to influence the media selection of users. Furthermore, they offer community-based recommendations and interfaces for browsing and searching for available media content. User experience in these social media settings involves complex, rich interactions with the media content and other participants in the community. In order to support such communities, it is useful to understand the factors that drive the users' engagement.
Providing effective personalized recommendations for relevant media content to a user in the context of a wider community is therefore a complex task. Existing services provide a static taxonomy of media types or genre. Such taxonomies serve as the means for users to express their interests and find adequate media. They provide media categories that are commonly adopted by the user community and, thus, can be used to characterize a user's media consumption behavior to some degree.
For example, in the case of a music service, a user can express an interest in songs of a certain genre. The music service can use this information by assuming that whilst the user may not necessarily want to repeat the same song, the person is likely to choose the next song to play from the same or a related genre. This can be used to drive personalized recommendations. However, even basic genre taxonomies may have a large number of categories and lead to sparse and ineffective representations of media consumption patterns.
Such genre-based recommendations do not capture the many complex factors underlying the consumption of media by a user. For example, in a song listening context, the user can select the next song as a result of the user's music listening habits, the layout of the user interface, the specific state of mind of the user, a recommendation from a friend, or many other factors or influences. However, computationally, it is not feasible to include all the variables to model the user behavior and the context characteristics for making predictions that closely match real time media consumption.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known media recommendation techniques.